In each column of Tables REF , REF , REF , and Table 2 in Appendix, we highlight the best and second best by the bold font and underscore, respectively. The mutual information fails to rank high-performing checkpoints to the top and even produces negative Pearson and Kendall correlations, probably because of the features' high dimensions. Reducing the feature dimensions by PCA significantly improves the mutual information's ranking performance; MI w/ PCA (\(\alpha \) =0.01) performs the second best Rel@1, Recall@3 and Rel@3 among the ranking methods in Group III, the checkpoints of heterogeneous neural architectures. Varying \(\alpha \)  in \(I_\alpha \)  mutual information estimator [1]} can control the trade-off between variance and bias. MI w/ and w/o PCA (\(\alpha \) =0.01) perform better than MI w/ and w/o PCA (\(\alpha \) =0.50), respectively. It indicates that neural checkpoint ranking requires low-bias MI estimator since smaller \(\alpha \)  means low-bias but high-variance estimation.
